Abstract
Abstract In this paper, a sparsity-aware hybrid target localization method in multiple-input-multiple-output (MIMO) radars from time difference of arrival (TDOA) and angle of arrival (AOA) measurements is proposed. This method provides a maximum likelihood estimate of target position by employing compressive sensing techniques. A blockwise approach is addressed in order to achieve better accuracy for a constant computational complexity. The mismatch problem due to grid discretization is also tackled by a dictionary learning technique. The Cramer–Rao lower bound for this model is derived as a benchmark. Numerical simulations are included to corroborate the theoretical developments.
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